In this project we want to investigate the correlation between the diets and beak measurements of birds and to figure out if you can predict a bird’s diet based on their beak measurements alone.
Bird beak anatomy can vary greatly in size and shape depending on habitat and diet (1). e.g.
Cone Shape: Picking up and cracking seeds easily.
Broader/Flatter: Larger surface area for bug catching in the air.
Tweezer-like: Bug eating off the ground.
Strong with Downward Point: Hooking onto meat easier.
Long/Thin & Spear-like Tip: Fish Catching.
figure 1: Bird Beak Anatomy
Bird beak anatomy is shown in figure 1.
The different diets of birds are sorted into different trophic levels. A trophic level defines an organism’s hierarchy in a ecosystem. The main three trophic levels are carnivores (eating >70% meat), omnivores (eating equal amounts of plants & meat) and herbivores (eating >70% plants).
We’ve left out scavenger birds since they make up <1% of our data, so using them may lead to false predictions.
The main trophic levels can be sub-categorised into niche trophics. Within these categories we found there to be a clearer link between the beak measurements and diets therefore we looked at the most common one, invertivores, to make our model.
In order to be categorised as one the following trophic levels the species must get at least 60% of its diet from the corresponding food resource:
| Trophic Niche | Diet Description |
|---|---|
| Frugivore | Fruit |
| Granivore | Seeds or Nuts |
| Nectarivore | Nectar |
| Herbivore | Plant Materials in Non-Aquatic Systems; i.e. leaves, buds, whole flowers etc. |
| Herbivore Aquatic | Plant Materials in Aquatic Systems; i.e. algae and aquatic plant leaves |
| Invertivore | Invertebrate Animals in Terrestrial Systems; i.e. insects, worms, arachnids, etc. |
| Vertivore | Vertebrate Animals in Terrestrial Systems; i.e. mammals, birds, reptiles etc. |
| Aquatic Predator | Vertebrate & Invertebrate Animals in Aquatic Systems; i.e. fish, crustacea, molluscs, etc. |
| Scavenger | Carrion (Dead Animal Corpses), Offal or Refuse |
| Omnivore | Species using multiple niches, within or across trophic levels, in relatively equal proportions |
Our main data set is the AVONET Supplementary Data Set called “birds_data” (2). It contains different bird species measurements and behavior; i.e. their habitat, diet and migration patterns.
The following variables are to be useful in our project:
Beak Length: The nares length is measured from the front edge of the nostril to the tip of the beak (see fig 2). We’ve chosen to focus on nares length over culmen length as beak width and depth also refers to the nostril location, whereas the culmen length refers to the skull.
Beak Width: Width of the beak at the front edge of the nostrils (see fig 2).
Beak Depth: Depth of the beak at the front edge of the nostrils (see fig 2).
General Trophic: Main diet level groups (Carnivore, Omnivore, Herbivore, Scavenger)
Niche Trophic: Niche diet level, sub categories of Tropic Level groups.
figure 2: Bird Measurements Taken
In order to tidy our original data set we checked for official NAs and located them, selected our required columns to reduce the variable amount, then renamed and reordered the columns to keep them clean and making sense.
During our project we have used a multitude of data science techniques. The main ones we have used are as follows:
We have tidied and wrangled the data in order to make it clear and concise for analysing.
We have imported the original data set and transcribed it into csv format to make it easy for us to read.
For data visualisation we have created many graphs to demonstrate our findings and data.
For our model, we’re using logistic regression as we’re predicting a catagorical value.
The trophic levels don’t have equal amounts of data, generally the carnivores have many more entries compared to any other column. We don’t believe this is due to sampling bias, just due to the fact there are likely more carnivore species of bird in existence compared to others.
When investigating the beak data, there are three variables to consider; beak length (nares), beak width and beak depth.
Summarising the data from these 3 columns we get:
## Beak_Nares_Length Beak_Width Beak_Depth
## Min. : 1.60 Min. : 0.700 Min. : 1.00
## 1st Qu.: 8.50 1st Qu.: 3.600 1st Qu.: 3.80
## Median : 11.70 Median : 5.000 Median : 5.80
## Mean : 17.06 Mean : 6.579 Mean : 8.06
## 3rd Qu.: 18.00 3rd Qu.: 7.700 3rd Qu.: 9.40
## Max. :389.80 Max. :88.900 Max. :110.90
We can visualise these values in a box plot whilst separating them into their respective General trophic levels:
Discounting the limited data for scavenger birds, these box plots shows that beak measurements don’t differ greatly between trophic levels on average.
As from above you can see that there wasn’t a clear distinction of the bird measurements between different general trophic levels. So we decided to have a look at the niche trophic levels.
The trophic with the least varied measurements across the beak are the nectarivores whereas the trophics with the most variation are the aquatic predators, scavengers, omnivores and herbivore terrestrials. This makes sense as nectarivores have a very niche diet, only really consisting of nectar, compared to the much more varied diet of scavengers and omnivores. This shows that a more varied diet creates higher beak size variation across the trophic levels.
The following graph summaries these mean beak data sets into values for each niche trophic level:
This further shows how birds with more variation in their diets have a higher variation of beak sizes and shapes.
This pie chart shows the proportion of niche trophics our data includes:
The majority of our trophic niche data is the invertivores, so we will be using that data for our model.
In order to answer our question of is it possible to predict diet based on beak measurements, we will need to use a logistic regression model since this is a discrete variable case.
Our first model, model-I1, uses only beak depth as a predictor value to predict whether the bird is an invertivore or not. We then made a second model, model-I for invertivores that uses all 3 predictor values (beak length, width and depth).
Here is the tidy model of model-I1, using 1 variable:
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.809 0.0434 18.7 1.13e- 77
## 2 Beak_Nares_Length -0.0579 0.00272 -21.3 1.02e-100
Here is the birds_fit_I1 model equation:
\[\log_{e}(\frac{p_{i}}{1-p_{i}}) = 0.809 - 0.058(Beak Nares Length)\]
Here is the tidy model of model-I, using 3 variables:
## # A tibble: 4 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 1.17 0.0490 23.9 2.84e-126
## 2 Beak_Nares_Length -0.0157 0.00292 -5.37 7.95e- 8
## 3 Beak_Width 0.151 0.0140 10.8 4.08e- 27
## 4 Beak_Depth -0.271 0.0127 -21.2 3.40e-100
Here is the birds_fit_I model equation:
\[\log_{e}(\frac{p_{i}}{1-p_{i}}) = 1.171 - 0.016(Beak Nares Length) + 0.151(Beak Width) - 0.271(Beak Depth)\]
The ROC of models I1 and 1 are below:
Regarding the area under the curve, model I1 has a value of 0.6806375 whereas model I has a value of 0.7395973.
Both values are above 0.5 which shows that both models are better than random chance but since model I has a higher area, it is considered the better model.
In terms of context, researchers probably have a similar model or method so that they can tell what birds eat without observation e.g looking at bones. This is similar to how they do this with dinosaurs.
The thought behind out model is that bird beaks have evolved and taken on different beak shapes to best eat there chosen food type. Models like this could be used in conjunction with other methods.
The model is binomial so can only predict success or failure for one food type. It can’t predict from a wide variety of varying trophics.
The model also isn’t perfect and would be better if it was within a certain range or proportion e.g beak width in proportion to beak length could provide more accurate results.
The following table shows the cutoff percentages, as you can see there are a lot of false positives:
| Bird is not Invertivore | Bird is Invertivore | |
|---|---|---|
| Bird labelled Invertivore | 932 | 688 |
| Bird labelled not Invertivore | 117 | 463 |
| Bird is not Invertivore | Bird is Invertivore | |
|---|---|---|
| Bird labelled Invertivore | 1000 | 978 |
| Bird labelled not Invertivore | 49 | 173 |
1 (Burleydam garden Centre):
2 (BirdLife 2020):
Accessed from figshare (URL: https://figshare.com/s/b990722d72a26b5bfead?file=38429885), accessed on 27/11/2023.
HBW-BirdLife Version 5.0 (December 2020). Handbook of the Birds of the World and BirdLife International (2020). Handbook of the Birds of the World and BirdLife International digital checklist of the birds of the world. Version 5. Available at: http://datazone.birdlife.org/userfiles/file/Species/Taxonomy/HBW-BirdLife_Checklist_v5_Dec20.zip
Figure 1:
Figure 2: